Leveraging Machine Learning for Bidding Strategies in Miner Extractable Value Auctions

TLDR

  • This paper analyzes changes in MEV bidding/auction behaviors. It shows that constant vs dynamic bidding strategies are optimal for different MEV attack vector. The paper proposes an ML model for bidding that wins over 50% of all Flashbot auctions.

Key learnings

  • The application of MEV strategies is not monolithic. That is to say, the type of MEV in question dictates the optimal strategies utilized by various participants.

  • ML strategies are well-suited to auction/bidding mechanisms and are easily optimizable and dynamic according to changes in participant and market behavior.

  • The ML strategy proposed in this paper is valuable and applicable to retail vault use cases for Concrete.

Concrete

  • Once retail vaults are implemented for Concrete coverage uses, bidding mechanics are highly relevant in order to optimize yield and liquidity curves.

  • If Concrete is successful, these auctions will be as competitive as MEV opportunities and structuring mechanisms to account for this competitiveness is vital prior to launch.

  • The ML model provided is generalizable and applicable to retail vault use cases for Concrete.

Details

  • Unlike P2P network broadcasting, MEV extractors using a relay service cannot observe their competitors’ bids during the auction, leading to the adoption of sealed-bidding strategies.

  • The closed/sealed nature of these auctions has been greatly exacerbated by Flashbot private pools.

  • MEV competitions are first-price sealed-bid auctions, which allows MEV extractors to privately communicate their bid and transaction order preference without paying for failed bids.

  • The auction mechanism tries to increase validator payoffs, while non-winning participants remain anonymous due to the sealed auction format.

  • The auction also provides guarantees such as pre-trade privacy and failed trade privacy. Bribes can either be paid through direct payment to the validator or higher gas fees.

  • Top 10 bots won ~50% of all auctions. This is extremely high.

  • MEV opportunities are categorized as sandwich attacks or cyclical arbitrages

    • Sandwich attacks are well defined - placing a transaction between two others to swap and profit on slippage.

    • 2 kinds of cyclical arbitrages which amount to the same results - profit based on swap differences.

    • Bribe ratios for both MEV options have approached, and stayed near, 100% value of profit.

  • ML model was trained on the following features:

    • Block number

    • Potential profit

    • Revenue with base fee

    • Number of swaps

    • Start amount

    • End amount

    • Base gas cost

    • Gas required

    • Protocols

    • Token categories

  • Resulting model was built on on an LGBM Regressor model. This model won 50%+ of all profit values, not just auctions.

  • Model shows that dynamic vs static models are not clearly hierarchical regarding value/profit. For example, sandwich attacks are better-suited for dynamic/adaptive bidding strategies.

  • This situation is analogous to how a retail vault may be structured for us. It is worth understanding models that we, or others, could use in participating with them.

Challenges/concerns/comments

  • This paper is of the highest quality - the methods are beyond reproach. No concerns.

  • The model utilized may not be easily adaptable to other auction use cases given its selection based on relevant features.

Further reading

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